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Construction and Application of Knowledge Graph for Quality and Safety Supervision of Transportation Engineering

Sheng Huang, Chuanle Liu*

Quality and Safety Supervision Bureau of Hunan Transportation Construction, Changsha, 410116, China

* Corresponding Author: Sheng Huang. Email: email

Journal on Artificial Intelligence 2021, 3(4), 153-162.


Knowledge graph technology play a more and more important role in various fields of industry and academia. This paper firstly introduces the general framework of the knowledge graph construction, which includes three stages: information extraction, knowledge fusion and knowledge processing. In order to improve the efficiency of quality and safety supervision of transportation engineering construction, this paper constructs a knowledge graph by acquiring multi-sources heterogeneous data from supervision of transportation engineering quality and safety. It employs a bottom-up construction strategy and some natural language processing methods to solve the problems of the knowledge extraction for transportation engineering construction. We use the entity relation extraction method to extract the entity triples from the multi-sources heterogeneous data, and then employ knowledge inference to complete the edges in the constructed knowledge graph, finally perform quality evaluation to add the valid triples to the knowledge graph for updating. Subgraph matching technology is also exploited to retrieve the constructed knowledge graph for efficiently acquiring the useful knowledge about the quality and safety of transportation engineering projects. The results show that the constructed knowledge graph provides a practical and valuable tool for the quality and safety supervision of transportation engineering construction.


Cite This Article

S. Huang and C. Liu, "Construction and application of knowledge graph for quality and safety supervision of transportation engineering," Journal on Artificial Intelligence, vol. 3, no.4, pp. 153–162, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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